Research shows that the substation noise is mainly the noise of large power transformer, and the power transformer noise comes from the transformer body and cooling system.
Transformer body noise mainly from the core vibration caused by the magnetostriction of silicon steel sheet and the electromagnetic force generated by the current flowing through the winding. The energy is mainly concentrated in the integer multiple frequency of fundamental frequency (100 Hz) below 500 Hz. It has the characteristics of strong penetrating ability, slow decay and so on. Transformer cooling system noise is mainly due to the fan and oil pump running vibration. The energy is mainly concentrated in the high frequency part above 500 HZ. This part of noise attenuation is fast. Transformer low frequency noise is the main cause of environmental pollution, control of this part of the noise is necessary.
Chinese patent CN102355233A uses fixed-frequency sine and cosine signals synthesized using direct digital synthesis as the reference signal which frequency value corresponds to the rated frequency of harmonic noise. CN102176668A directly digitally synthesizes a corresponding reference signal according to the fundamental frequency component of the picked-up primary noise. The above patents use digital direct synthesize reference signal, can only reduce the noise of some frequency components. Although the traditional F-XLMS filter algorithm can suppress all the frequency components of noise, but it cannot deal with time-varying, nonlinear noise. BP neural network has the ability of nonlinear processing, self-adaptability and robustness. Therefore, scholars apply the BP neural network algorithm to the noise reduction field. Traditional BP neural network inherent convergence speed is slow and is easy to fall into the local minimum, so that the effect of noise control is not ideal. Therefore, we need to find a better filtering algorithm to achieve better noise reduction effect.